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McLean, A. R.

Publications and source records attributed to McLean, A. R..

2 recordsLinked to original sources

Site-specificity of streptococci in the oral microbiome

Patterns of microbial distribution are determined by as-yet poorly understood rules governing where microbes can grow and thrive. Therefore, a detailed understanding of where bacteria localize is necessary to advance microbial ecology and microbiome-based therapeutics. The site-specialist hypothesis predicts that most microbes in the human oral cavity have a primary habitat within the mouth where they are most abundant. We asked whether this hypothesis accurately describes the distribution of the members of the genus Streptococcus, a clinically relevant taxon that dominates most oral sites. Prior analysis of 16S rRNA gene sequencing data indicated that some oral Streptococcus clades are site-specialists while others may be generalists. However, within complex microbial populations composed of numerous closely-related species and strains, such as the oral streptococci, genome-scale analysis is necessary to provide the resolution to discriminate closely related taxa with distinct functional roles. Here we assess whether individual species within this genus are generalists using publicly available genomic sequence data that provides species-level resolution. We chose a set of high-quality representative genomes for Streptococcus species from the human oral microbiome. Onto these genomes, we mapped short-read metagenomic sequences from supragingival plaque, tongue dorsum, and other sites in the oral cavity. We found that every reliably detectable Streptococcus species in the human oral cavity was a site-specialist and that even closely related species such as S. mitis, S. oralis, and S. infantis specialized in different sites. These findings indicate that closely related bacteria can have distinct habitat distributions in the absence of dispersal limitation and under similar environmental conditions and immune regimes. These three species also share substantially the same species-specific core genes indicating that neither taxonomy nor gene content are clear predictors of site-specialization. Site-specificity may instead be influenced by subtle characteristics such as nucleotide-level divergences within conserved genes.

microbiology↗

Mapping the drivers of within-host pathogen evolution using massive data sets

Differences among hosts, resulting from genetic variation in the immune system or heterogeneity in drug treatment, can impact within-host pathogen evolution. Identifying such interactions can potentially be achieved through genetic association studies. However, extensive and correlated genetic population structure in hosts and pathogens presents a substantial risk of confounding analyses. Moreover, the multiple testing burden of interaction scanning can potentially limit power. To address these problems, we have developed a Bayesian approach for detecting host influences on pathogen evolution that makes use of vast existing data sets of pathogen diversity to improve power and control for stratification. The approach models key processes, including recombination and selection, and identifies regions of the pathogen genome affected by host factors. Using simulations and empirical analysis of drug-induced selection on the HIV-1 genome we demonstrate the power of the method to recover known associations and show greatly improved precision-recall characteristics compared to other approaches. We build a high-resolution map of HLA-induced selection in the HIV-1 genome, identifying novel epitope-allele combinations.

genetics↗